Genetic and behavioral analysis has been developed for identifying the function of genes. For example, numerous methods are developed in effort for disease modeling, such as the production of germ-line transgenic animal models (e.g. transgenic mice and other animals with specific genetic characters). However, practically, a major obstacle in performing gene/disease analysis on transgenic mammals is the long life span of animals. It takes too long (a few years at least) in the laboratory to trace diseases evolving from abnormal genes in one animal. Many researches on disease treatments could be delayed because scientists had not a swift and easy access to the source of pathogenesis at the molecular level for accurate identification. One remedy for the situation is to utilize some relevant systems in short life-spanned (only days from birth to mature) insects as models. For example, the brain of fruit flies (Drosophila melanogaster) has been used to investigate the pathogenesis of Alzheimer disease. Please refer to the article “A Drosophila model of Alzheimer's disease: dissecting the pathological roles of Aβ42 and Aβ40”, to K. Iijima, Proc. Natl. Acad. Sci. USA, vol. 101, 6623-6628, 2004. Likewise, studies on early detection and treatment of numerous diseases may become more efficient in the future if a good correlation among genes, cellular structures and diseases can be established successfully in a fly model. The benefit resulted will be not only on science but also on public health that many new treatments with better accuracy can be found for diseases, especially for those are gene related.
Up to now, no research or application employs the computer-assisted system to inspect the real environment at the cellular level in biotechnology, although some applications relate to computerized medical diagnostic systems are available. New technology is needed to help observing the wholeness of the article at its finest level that cannot be detected by the conventional optical technology. Recently, three-dimension reconstruction technology has been developed that allows rebuilding the three dimensional image of cells, thereby providing the tool to understand the fine structure of them. However, such technology focuses on a single cell or only few cells cultured in an artificial environment (in vitro) instead of in the real body (in vivo). The difference between the two environments is more significant in the field of neural science. The distribution of neurons is really three-dimensional in the body, but they are plated in a two-dimensional situation in cell cultures. It is unlikely that a two-dimensional environment simulates the three-dimensional neural networks in the body. Currently, some approaches struggle to observe the neural system in three-dimensional environment. However, the approaches are restricted by the penetration depth of the optical system that is hardly capable of looking through the depth deeper than 50-micron meter by using visible light. One attempt for reconstruction of individual wild type Drosophila larval and adult brains was described in Karlheinz Rein, Peter Robin Hiesinger, Malte Zöckler, Jan Kirsten, Karl-Friedrich Fischbach, and Martin Heisenberg. (2000), “Three-dimensional Reconstruction of the Drosophila Larval and Adult Brain”, FLYBRAIN. acc. AB00120-AB00127, available from University of Arizona, identified as “neurobio.arizona.edu” (2000 Flybrain, compiled by Hiesinger, Peter Robin). In similar studies, the fruit fly has become one of the prime model systems in brain research. Its brain (about 600.times.250.times.150 micrometers) consists of about 200,000 neurons. Given this relatively small brain, the fly shows a surprisingly complex repertoire of behaviors, e.g. orientation, courtship, learning and memory. The whole brains were dissected from heads, sliced and labeled fluorescently for inspections. However, in this way and in all prior methods, the whole neural circuitry in the fly brain is impossible to be reconstructed reasonably due to its physical damages from tissue slicing and the limited depth of view in each observation. Our invention provides a complete and novel resolution to overcome this barrier.
Virtual reality technology has progressed into practical and useful applications. These applications have found utility in a wide variety of fields and industries. One application is known as training and researching applications. Virtual reality training applications allow users to develop important skills and experience without subjecting them to the hazards or costs of training. Virtual reality is a computer-generated environment in which a user is immersed. Actions of the user are translated by a computer into inputs that effect the virtual environment (VE). Virtual reality systems may stimulate naturally occurring senses, so that a user can navigate through a virtual environment as if in the real world. However, never a virtual reality system has been used to explore the cellular networks in a biological tissue at high resolution (in the range of few micrometers).
Although images of biological cells at high resolution have been available for a long time but hardly any cellular network (such as neural networks in an intact brain) has been revealed anatomically, let along interactions among different networks. Current technology for medical imaging is capable of generating series of images for database construction, however not only the revelation of cellular structures are not intended but also the genetic information is hardly associated with the anatomy. Therefore, our purpose is to provide a way of combining high resolution biological structural database (such as neural networks), system of gene (protein) expression in biological tissues and a visual demonstration in virtual reality. Such a system should be modular to allow expansion for multiple types of gene (protein) expression to correlate the anatomy and the function (or dysfunction) to the molecular level. In this way, the correlations among genes, cellular networks and biological functions can be examined and manipulated in the most realistic environment. Biological function simulation may be achieved when detailed cellular networks and genetic information are realistically available.